Eviews5 / EViews5 / Example Files / x12 / example7

12 November 1998

Example 7: The specs in this example demonstrate some of the strategies
we have found for dealing with residual spectrum peaks. Some
of the strategies listed below don't have an associated example
spec file.

Strategies Against Spectrum Peaks at Seasonal Frequencies in
the spectrum for RegARIMA Residuals --
(1) Change the ARIMA model, e.g., add a seasonal AR term.
(2) Change the regressors.
(a) Remove fixed seasonal regressors and add a seasonal
ARIMA model, such as (0 1 1)12.
(b) Add outliers that are just below the critical value.
(3) Change the data, e.g., remove all but the last 8-10 years
of the series.

Strategies Against Spectrum Peaks at Trading Day Frequencies
in the spectrum for RegARIMA Residuals, the seasonally adjusted
data, or the irregulars --
(1) Add TD variables to the regression if none were included
or if those tried were rejected by an AIC test. (Setting
insignificant coefficients to zero can sometimes change the
aictest result from rejection to acceptance.)
(2) Choose a different type of TD regressor, such as a stock
trading day variable (tdstock), a weekend/weekday trading
day variable (td1coef), or a user-defined trading day
variable. You may also want to try a change-of-regime
version of any of the trading day variables.

Suggested graphs:
A graph of the original series to help find a shorter span,
spectrum graphs, and sum of squared forecast error history graphs.


The Italian Export Quantity Index (CETGENGQ) --

The spec file cetg0.spc contains a default run of the Italian
Export Quantity index. The aictest rejects trading day. The
spectrum of the both the RegARIMA residuals and the irregular
contains trading day peaks. (This series has already been
shortened from 1980.jan to 1986.jan. Remove the span
statement and graph the original series to see why the series
was shortened.)

The aictest for trading day rejects trading day based on the AIC.
We compared all the trading day options (Flow TD, Stock TD,
Weekday/Weekend TD, and No TD) using AIC history graphs and Sum
of Squared Forecast Error graphs in X-12-Graph. Stock TD had
the lowest out-of-sample forecast error. AICs for the stock
trading day can be improved by using the b option in the
regression spec and fixing some of the coefficients to zero.
The example spec file with Stock trading day added is cetg1.spc.

With the trading day included, there are both seasonal and
trading day peaks in the RegARIMA residuals. We had some
success in reducing the size of the seasonal peak by adding
a seasonal AR(1) term to the model. However, even without the
seasonal AR(1) term, when we added regressors for the outliers
with the largest absolute t-values, the seasonal peak was
eliminated. Our final options are in cetgengq.spc.

US Single Family Housing Starts for Northeast and Midwest Regions --

ne1f0.spc shows a run for the Northest Single Family Housing
Starts series. A seasonal peak was found in the RegARIMA
residuals. A graph of both series suggests that the series has
changed significantly in recent years. Shortening the series
changes the ARIMA model coefficients and eliminates the seasonal
spectrum peak.

mw1f0.spc shows a run for the Midwest region. A trading day peak
was found in the RegARIMA residuals. There is evidence of
deminishing trading day effects in the later part of the series.
A change-of-regime trading day regressor eliminates the
trading day spectrum peak.



# Example 7: cetgengq.spc

# Our final settings for this series.


series{
name="CETGENGQ"
start=1980.1
period=12
file="cetgengq.dat"
title="CETGENGQ: Airline Model with Outliers and Stock TD"
span=(1986.1,)
decimals=1
}
transform{function=log}
regression{
variables=(tdstock[31]
ao1987.3 ls1987.7 ao1988.1 ao1993.8 )
b=(0. 0.0f 0.0f 0. 0. 0.0f
0. 0. 0. 0.)
}
arima{model=(0 1 1)(0 1 1)}
estimate{}
check{print=all savelog=lbq}
forecast{maxlead=24 print=none}
x11{
savelog=(q q2 m7 m10 m11)
}
slidingspans {
print=(default +pcy +suy)
savelog=percent
}
#history{
# estimates=(sadj sadjchng trend)
# fixmdl=no
# start=1994.1
#}